PowerTap: All-digital power meter modeling for run-time power monitoring

Zoni, Davide; Cremona, Luca; Cilardo, Alessandro; Gagliardi, Mirko; Fornaciari, William

doi:10.1016/j.micpro.2018.07.007

Cited by 19 publications

(24 citation statements)

References 26 publications

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“…As a future extension, we will investigate the possibility of running the method incrementally and on embedded multicores, coupling it with a real-time 3D reconstruction method [23] also accounting the energy constraints imposed by the computing platform [40,41]. We also plan to test the method with a wider dataset such as ScanNet ( [9]), by replacing the Multiboost algorithm, that failed to estimate a reasonable segmentation of ScanNet scenes, with a more sophisticated image segmentation algorithm based on neural networks.…”

Section: Discussionmentioning

confidence: 99%

A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling

Romanoni

Matteucci

2018

2018 International Conference on 3D Vision (3DV)

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Mesh labeling is the key problem of classifying the facets of a 3D mesh with a label among a set of possible ones. State-of-the-art methods model mesh labeling as a Markov Random Field over the facets. These algorithms map image segmentations to the mesh by minimizing an energy function that comprises a data term, a smoothness terms, and classspecific priors. The latter favor a labeling with respect to another depending on the orientation of the facet normals. In this paper we propose a novel energy term that acts as a prior, but does not require any prior knowledge about the scene nor scene-specific relationship among classes. It bootstraps from a coarse mapping of the 2D segmentations on the mesh, and it favors the facets to be labeled according to the statistics of the mesh normals in their neighborhood. We tested our approach against five different datasets and, even if we do not inject prior knowledge, our method adapts to the data and overcomes the state-of-the-art.

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Section: Discussionmentioning

confidence: 99%

A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling

Romanoni

Matteucci

2018

2018 International Conference on 3D Vision (3DV)

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“…In particular, their implementation minimizes the performance overhead typically associated with the software-implemented schemes that, unfortunately, compete with the other applications for the CPU. Hardware solutions can also increase the timing resolution of the entire monitoring system to efficiently manage faster physical phenomena [5], [6], [7]. Note that our proposal targets the design of a controller that leverages on the all-digital scheme proposed in [6] as the online power monitor whose detailed description is out of the scope of this research.…”

Section: Background and Related Workmentioning

confidence: 99%

“…Nevertheless, it can be detrimental for the performance of embedded platforms, since part of the computing resources must be devoted to the computation of both the power estimate and the control action. Recent investigations [5], [6], [7] proposed all-digital power monitoring solutions that estimate the consumed power from the switching activity of selected signals at the microarchitectural level. However, there is a complete absence of a unified and general methodology to control different energy related aspects, also considering applications and Operating System requirements.…”

Section: Introductionmentioning

confidence: 99%

“…The controller has been implemented in hardware and assessed on 4-and an 8-core processors via the Digilent Nexys4-DDR board featuring a Xilinx Artix7 100t FPGA device. To demonstrate the effectiveness of the proposed solution, we employed the online power monitoring infrastructure presented in [6] and the power-cap PID controller presented in [1]. In particular, the proposed multi-level controller advances the state-of-the-art in three main directions:…”

Section: Introductionmentioning

confidence: 99%

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All-Digital Control-Theoretic Scheme to Optimize Energy Budget and Allocation in Multi-Cores

Zoni

Cremona

Fornaciari

2020

IEEE Trans. Comput.

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The Internet-of-Things (IoT) revolution fueled new challenges and opportunities to achieve computational efficiency goals. Embedded devices are required to execute multiple applications for which a suitable distribution of the computing power must be adapted at run-time. Such complex hardware platforms have to sustain the continuous acquisition and processing of data under severe energy budget constraints, since most of them are battery powered. The state-of-the-art offers several ad-hoc contributions to selectively optimize the performance considering aspects like energy, power, thermal or reliability. However, there is a need for a generic coordinated management strategy able to cope with all of these dimensions, while allowing the Operating System (OS) and the applications to "suggest" or constrain the actuation. This paper proposes a unified control-theoretic scheme to coordinate the design of energy-budget and energy allocation solutions for multi-cores. The proposed controller can work with any actuator and it can interact, at run-time, with both the applications and the OS to optimize the actuation signals steering the computing platform. Such control scheme offers the possibility to integrate any performance related policy in the form of an energy-allocation strategy, still ensuring the theoretic exponential stability of the overall controller if the actuation of the policy, coming from the OS and the applications, "is not too fast". To demonstrate the feasibility of our solution, we implemented the controller into a RISC multi-core running on the Xilinx Artix 100t FPGA device, available in the the Digilent Nexys4-DDR board. Results considering two actuators and both the quad-and the eight-core version of the considered computing platform, highlight the scalability of the proposed solution as well as an area overhead for the-all digital, on chip-controller limited to 0.86% (FFs) and 5.3% (LUTs) of the FPGA chip. We also considered a dynamic scenario validating the speed of the controller, where our framework has to face with modifications to the energy-allocation control policy carried out by the OS and the applications. The obtained results are collected by executing a huge mix of benchmarks and the statistical significance is accounted by executing each scenario 30 times. Such results are analyzed considering three quality metrics. First, the efficiency in exploiting the imposed budget (EF Fg) that is on average 98.27%. Second, the overflow of the actual average power consumption with respect to the assigned budget (OV Fg), which is limited to 1.43 mW on average. Last, the performance utility loss due to the control scheme that is limited to 1.87% on average.

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“…This approach combines power capping with coordinated dynamic voltage and frequency scaling (DVFS), data partitioning and core allocations for efficient use of both ARM processor and streaming accelerators on FPGA concurrently. A similar approach, leveraging a hardware implementation for the power capping, was proposed in [30,33], based on the modeling of DVFS and power gating actuators provided in [34]. A run-time task allocator for heterogeneous many-core platforms, SPARTA, was presented in [6].…”

Section: Ldpc Ifs Solution Id Processors Memoriesmentioning

confidence: 99%

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

Massari

Pupykina

Agosta

et al. 2019

Lecture Notes in Computer Science

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High-Performance Computing (HPC) is rapidly moving towards the adoption of nodes characterized by an heterogeneous set of processing resources. This has already shown benefits in terms of both performance and energy efficiency. On the other side, heterogeneous systems are challenging from the application development and the resource management perspective. In this work, we discuss some outcomes of the MANGO project, showing the results of the execution of real applications on a emulated deeply heterogeneous systems for HPC. Moreover, we assessed the achievements of a proposed resource allocation policy, aiming at identifying a priori the best resource allocation options for a starting application.

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PowerTap: All-digital power meter modeling for run-time power monitoring

Cited by 19 publications

References 26 publications

A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling

A Data-Driven Prior on Facet Orientation for Semantic Mesh Labeling

All-Digital Control-Theoretic Scheme to Optimize Energy Budget and Allocation in Multi-Cores

Predictive Resource Management for Next-Generation High-Performance Computing Heterogeneous Platforms

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